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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題:

1. The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
C) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
D) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

2. A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A) Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
B) Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
C) Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
D) Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
E) Merge all changes back to the main branch in the remote Git repository and clone the repo again

3. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A) Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
B) Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C) Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D) All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E) Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

4. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B) Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C) Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

5. The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A) Define a view against the products_per_order table and define the dashboard against this view.
B) Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C) Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
D) Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
E) Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.

問題與答案：